There are many types of video signals, such as digital broadcast television (TV), video conferencing, interactive TV, etc. All of these signals, in their digital form, are divided into frames, each of which consists of many pixels (image elements), each of which requires 8-24 bits to describe them. The result is megabits of data per frame.
Before storing and/or transmitting these signals, they typically are compressed, using one of many standard video compression techniques, such as JPEG, MPEG, H-compression, etc. These compression standards use video signal transforms and intra- and inter-frame coding which exploit spatial and temporal correlations among pixels of a frame and across frames.
However, these compression techniques create a number of well-known, undesirable and unacceptable artifacts, such as blockiness, low resolution and wiggles, among others. These are particularly problematic for broadcast TV (satellite TV, cable TV, etc.) or for systems with very low bit rates (video conferencing, videophone).
Much research has been performed to try and improve the standard compression techniques. The following patents and articles discuss various prior art methods to do so:
U.S. Pat. Nos. 5,870,501, 5,847,766, 5,845,012, 5,796,864, 5,774,593, 5,586,200, 5,491,519, 5,341,442;
Raj Talluri et al, “A Robust, Scalable, Object-Based Video Compression Technique for Very Low Bit-Rate Coding,” IEEE Transactions of Circuit and Systems for Video Technology, vol. 7, No. 1, February 1997;
AwadKh. Al-Asmari, “An Adaptive Hybrid Coding Scheme for HDTV and Digital Sequences,” IEEE Transactions on Consumer Electronics, vol. 42, No. 3, pp. 926-936, August 1995;
Kwok-tung Lo and Jian Feng, “Predictive Mean Search Algorithms for Fast VQ Encoding of Images,” IEEE Transactions On Consumer Electronics, vol. 41, No. 2, pp. 327-331, May 1995;
James Goel et al. “Pre-processing for MPEG Compression Using Adaptive Spatial Filtering”, IEEE Transactions On Consumer Electronics, vol. 41, No. 3, pp. 687-698, August 1995;
Jian Feng et al. “Motion Adaptive Classified Vector Quantization for ATM Video Coding”, IEEE Transactions on Consumer Electronics, vol. 41, No. 2, p. 322-326, May 1995;
Austin Y. Lan et al., “Scene-Context Dependent Reference—Frame Placement for MPEG Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, No.3, pp. 478-489, April 1999;
Kuo-Chin Fan, Kou-Sou Kan, “An Active Scene Analysis-Based approach for Pseudoconstant Bit-Rate Video Coding”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 8 No.2, pp. 159-170, April 1998;
Takashi Ida and Yoko Sambansugi, “Image Segmentation and Contour Detection Using Fractal Coding”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, No. 8, pp. 968-975, December 1998;
Liang Shen and Rangaraj M. Rangayyan, “A Segmentation-Based Lossless Image Coding Method for High-Resolution Medical Image Compression,” IEEE Transactions on Medical Imaging, vol. 16, No. 3, pp. 301-316, June 1997;
Adrian Munteanu et al., “Wavelet-Based Lossless Compression of Coronary Angiographic Images”, IEEE Transactions on Medical Imaging, vol. 18, No. 3, p. 272-281, March 1999; and
Akira Okumura et al., “Signal Analysis and Compression Performance Evaluation of Pathological Microscopic Images,” IEEE Transactions on Medical Imaging, vol. 16, No. 6, pp. 701-710, December 1997.